IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i13p7565-d843812.html
   My bibliography  Save this article

Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong

Author

Listed:
  • Weijun Yu

    (Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA)

  • Cheryll Alipio

    (Walter H. Shorenstein Asia-Pacific Research Center, Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA 94305, USA)

  • Jia’an Wan

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY 14850, USA)

  • Heran Mane

    (Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA)

  • Quynh C. Nguyen

    (Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA)

Abstract

Background: Domestic workers, flight crews, and sailors are three vulnerable population subgroups who were required to travel due to occupational demand in Hong Kong during the COVID-19 pandemic. Objective: The aim of this study was to explore the social networks among three vulnerable population subgroups and capture temporal changes in their probability of being exposed to SARS-CoV-2 via mobility. Methods: We included 652 COVID-19 cases and utilized Exponential Random Graph Models to build six social networks: one for the cross-sectional cohort, and five for the temporal wave cohorts, respectively. Vertices were the three vulnerable population subgroups. Edges were shared scenarios where vertices were exposed to SARS-CoV-2. Results: The probability of being exposed to a COVID-19 case in Hong Kong among the three vulnerable population subgroups increased from 3.38% in early 2020 to 5.78% in early 2022. While domestic workers were less mobile intercontinentally compared to flight crews and sailors, domestic workers were 1.81-times in general more likely to be exposed to SARS-CoV-2. Conclusions: Vulnerable populations with similar ages and occupations, especially younger domestic workers and flight crew members, were more likely to be exposed to SARS-CoV-2. Social network analysis can be used to provide critical information on the health risks of infectious diseases to vulnerable populations.

Suggested Citation

  • Weijun Yu & Cheryll Alipio & Jia’an Wan & Heran Mane & Quynh C. Nguyen, 2022. "Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong," IJERPH, MDPI, vol. 19(13), pages 1-25, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7565-:d:843812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/13/7565/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/13/7565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Anming, 2003. "Analysis of an international air-cargo hub: the case of Hong Kong," Journal of Air Transport Management, Elsevier, vol. 9(2), pages 123-138.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krzysztof Rząsa & Mateusz Ciski, 2022. "Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression," IJERPH, MDPI, vol. 19(19), pages 1-26, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, L.C. & Hong, C.H., 2006. "Operational performance evaluation of international major airports: An application of data envelopment analysis," Journal of Air Transport Management, Elsevier, vol. 12(6), pages 342-351.
    2. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    3. Mayer, Robert, 2016. "Airport classification based on cargo characteristics," Journal of Transport Geography, Elsevier, vol. 54(C), pages 53-65.
    4. Gong, Qiang & Wang, Kun & Fan, Xingli & Fu, Xiaowen & Xiao, Yi-bin, 2018. "International trade drivers and freight network analysis - The case of the Chinese air cargo sector," Journal of Transport Geography, Elsevier, vol. 71(C), pages 253-262.
    5. Gardiner, John & Ison, Stephen & Humphreys, Ian, 2005. "Factors influencing cargo airlines’ choice of airport: An international survey," Journal of Air Transport Management, Elsevier, vol. 11(6), pages 393-399.
    6. Chao, Ching-Cheng & Yu, Po-Cheng, 2013. "Quantitative evaluation model of air cargo competitiveness and comparative analysis of major Asia-Pacific airports," Transport Policy, Elsevier, vol. 30(C), pages 318-326.
    7. Min Ju Bae & Ek Peng Chew & Loo Hay Lee & Anming Zhang, 2013. "Container transshipment and port competition," Maritime Policy & Management, Taylor & Francis Journals, vol. 40(5), pages 479-494, September.
    8. Yang, Jinglei & Luo, Meifeng & Ji, Abing, 2016. "Analyzing the spatial–temporal evolution of a gateway’s hinterland: A case study of Shanghai, China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 355-367.
    9. Tsui, Wai Hong Kan & Fung, Michael Ka Yiu, 2016. "Analysing passenger network changes: The case of Hong Kong," Journal of Air Transport Management, Elsevier, vol. 50(C), pages 1-11.
    10. Yuen, Andrew & Zhang, Anming & Hui, Yer Van & Leung, Lawrence C. & Fung, Michael, 2017. "Is developing air cargo airports in the hinterland the way of the future?," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 15-25.
    11. Matsumoto, Hidenobu & Domae, Koji, 2019. "Assessment of competitive hub status of cities in Europe and Asia from an international air traffic perspective," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 88-95.
    12. Singh, D.P. & Dalei, Narendra N. & Raju, T. Bangar, 2016. "Forecasting investment and capacity addition in Indian airport infrastructure: Analysis from post-privatization and post-economic regulation era," Journal of Air Transport Management, Elsevier, vol. 53(C), pages 218-225.
    13. Lo, Winnie Wai Ling & Wan, Yulai & Zhang, Anming, 2015. "Empirical estimation of price and income elasticities of air cargo demand: The case of Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 309-324.
    14. Cui, Qiang & Kuang, Hai-bo & Wu, Chun-you & Li, Ye, 2013. "Dynamic formation mechanism of airport competitiveness: The case of China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 47(C), pages 10-18.
    15. Masilonyane Mokhele & Tholang Mokhele, 2023. "Characterization of Airfreight-Related Logistics Firms in the City of Cape Town, South Africa," Logistics, MDPI, vol. 7(3), pages 1-21, July.
    16. Cheung, Tommy King-Yin & Wong, Wai-hung & Zhang, Anming & Wu, Yangming, 2020. "Spatial panel model for examining airport relationships within multi-airport regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 148-163.
    17. Hui, George W.L & Hui, Yer Van & Zhang, Anming, 2004. "Analyzing China's air cargo flows and data," Journal of Air Transport Management, Elsevier, vol. 10(2), pages 125-135.
    18. Huynh, Triet Minh & Kim, Gyuseung & Ha, Hun-Koo, 2020. "Comparative analysis of efficiency for major Southeast Asia airports: A two-stage approach," Journal of Air Transport Management, Elsevier, vol. 89(C).
    19. Kan Tsui, Wai Hong & Balli, Hatice Ozer & Gilbey, Andrew & Gow, Hamish, 2014. "Operational efficiency of Asia–Pacific airports," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 16-24.
    20. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7565-:d:843812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.